Enterprises often rely on input/output (IO) intensive networked applications running on dedicated servers that support associated “state”, context or session-defined applications. Servers can run multiple applications, each associated with a specific state running on the server. Common server applications include an Apache web server, a MySQL database application, PHP hypertext preprocessing, video or audio processing with Kaltura supported software, packet filters, application cache, management and application switches, accounting, analytics, and logging.
Unfortunately, servers can be limited by computational and memory storage costs associated with switching between applications. When multiple applications are constantly required to be available, the overhead associated with storing the session state of each application can result in poor performance due to constant switching between applications. Dividing applications between multiple processor cores can help alleviate the application switching problem, but does not eliminate it. Advanced processors often only have eight to sixteen cores, while hundreds of application or session states may be required.
Enterprises also store and process their large amounts of data in a variety of ways. One manner in which enterprises store data is by using relational databases and corresponding relational database management systems (RDBMS). Such data, usually referred to as structured data, may be collected, normalized, formatted and stored in an RDBMS. Tools based on standardized data languages such as the Structured Query Language (SQL) may be used for accessing and processing structured data. However, it is estimated that such formatted structured data represents only a tiny fraction of an enterprise's stored data. All organizations are becoming increasingly aware that substantial information and knowledge resides in unstructured data (i.e. “Big Data”) repositories. Accordingly, simple and effective access to both structured and unstructured data are seen as necessary for maximizing the value of enterprise informational resources.
However, the platforms that are currently being used to handle structured and unstructured data substantially differ in their architecture. In-memory processing and Storage Area Network (SAN)-like architecture are used for traditional SQL queries, while commodity or shared nothing architectures (each computing node, consisting of a processor, local memory, and disk resources, shares nothing with other nodes in the computing cluster) are usually used for processing unstructured data.
A computing system architecture, hardware, and operational method that supports input-output (IO) intensive networked applications, as well as structured and unstructured data queries is needed. Such a system needs to readily handle high throughput data processing, be able to provide high parallelism for dividing tasks among multiple processors, and further provide efficient context switching to support multiple users or applications.